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With the advances and growth of various wireless technologies, it is imperative to implement robust Intrusion Detection Systems (IDS). This paper proposes the implementation of Deep Gated Recurrent Unit (DGRU) Based classifier as well as a wrapper-based feature extraction algorithm for Wireless IDS. We assess the performance of the DRGU IDS using the NSL-KDD benchmark dataset. Furthermore, we compare our framework to several popular algorithms including Artificial Neural Networks, Deep Long–Short Term Memory, Random Forest, Naive Bayes and Feed Forward Deep Neural Networks. The experimental outcomes demonstrate that the DGRU IDS displays a significant increase in performance over existing methods.

Wireless networks have evolved over the years and they have become some of the most prominent communication media. These networks generally transmit large volumes of information at any given time. This has engendered a number of security threats and privacy concerns. This paper presents a Deep Long Short-Term Memory (DLSTM) based classifier for wireless intrusion detection system (IDS). Using the NSL-KDD dataset, we compare the DLSTM IDS to existing methods such as Deep Feed Forward Neural Networks, Support Vector Machines, k-Nearest Neighbors, Random Forests and Naïve Bayes. The experimental results suggest that the DLSTM IDS outperformed existing approaches.